{"id":"W2971361494","doi":"10.1109/tvt.2019.2939145","title":"Crowd Sensing in Vehicular Networks Using Uncertain Mobility Information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mobility model; Correctness; Bottleneck; Scheduling (production processes); Channel (broadcasting); Real-time computing; Noise (video); Distributed computing; Computer network; Mathematical optimization; Algorithm; Artificial intelligence; Embedded system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005155255,0.0002713151,0.0003482005,0.0008598856,0.0002225448,0.0001386149,0.0004787002,0.0005086694,0.000009373171],"category_scores_gemma":[0.00001412694,0.0002862994,0.0001364928,0.001699197,0.0001140047,0.0007134898,0.00001243739,0.0008500604,0.00006809026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002938563,"about_ca_system_score_gemma":0.00009273479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001482774,"about_ca_topic_score_gemma":0.00004088402,"domain_scores_codex":[0.9980305,0.0001190252,0.0005034119,0.0005127192,0.0002767116,0.0005576402],"domain_scores_gemma":[0.9984541,0.00007742306,0.0001398672,0.001128257,0.0001315143,0.0000688326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000108755,0.00006823065,0.0001636839,0.00002255379,0.00002023795,0.00002938488,0.0001489689,0.9288629,0.005925998,0.0006230331,0.00000346635,0.06412067],"study_design_scores_gemma":[0.000548469,0.00009097299,0.0001151259,0.0001211835,0.00001235044,0.0001507142,0.0001516603,0.9726105,0.02491481,0.0004787769,0.0004997424,0.0003056656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4469332,0.00003599391,0.5517697,0.0002272654,0.0003905134,0.0002636955,7.149146e-7,0.0003303029,0.00004862312],"genre_scores_gemma":[0.9847767,0.00001081383,0.01490894,0.0002389078,0.00001415287,0.00001093627,0.000001703717,0.00001691283,0.0000208878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5378435,"threshold_uncertainty_score":0.9999589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008928871837124428,"score_gpt":0.223208380132654,"score_spread":0.2142795082955296,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}